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Don’t call yourself a “data scientist”

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Someone introduced himself recently as a “data scientist” to a data warehouse pro I know. “I thought he was a fool,” said Interworks consultant Tim Costello. He says it’s a meaningless term — and I believe it’s another one of those distractions thrown around in a roiling industry.

One of the most interesting of the others I’ve heard is Scott Davis, the founder of Lyzasoft. He emailed this week from somewhere on the Caribbean, “Data scientist is just a term someone applied to a set of skills and practices that have existed for a very, very long time. Like, since the invention of the abacus.”

What does he say to those who think it’s an attempt to distinguish between data analysts of any level and those at the top of the discipline? “The attempt is not by someone who does this stuff,” he writes. “It is by someone who is selling something…either a book or a training regimen or a system or a degree or a tool or….”

I looked for Stephen Few’s opinion, and of course he had one. In his 2,500-word blog post “Are you a data scientist?,” he cites interesting articles and poses good arguments, and essentially agrees with Scott and Tim. “There is indeed a science to data sensemaking,” he writes at the end, “but data science by any other name (and there are many) would smell as sweet.”

But why do we care? We do waste a lot of breath on terminology that means little one way or another. But sometimes it does matter — such as in Jill Dyché’s now-famous blog post “Why I wouldn’t have sex with a data scientist.”

Was he too busy, as she originally thought? Or was the poor guy just too full of himself? Whichever, it demonstrates natural selection the way it’s supposed to work.

Two more execs on data analysts in the executive suite

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An insurance executive I quoted a few weeks ago in this space spoke heresy: Not only do most data analysts he’s known lack the polish for presentations to execs, even worse was too narrow a point of view for leading an organization. It’s the Analytics Gap.

To that, the prolific tweeter and data scientist @data_nerd, Carla Gentry, reacted for many others in the decision support industry: “Huh?”

“I think a logical mind is perfect for business,” she tweeted to me when I inquired. “You must see the whole picture to run a successful business. Having an analytical mind helps.”

All true. Even the insurance executive and two others of that level — none of whom agreed to be named — would agree. The difference comes in the contrasting views of the “whole picture.”

Carla seems to assume that the whole picture can show itself in the data. The execs argue that it takes experience and judgement to see the real thing. “The conceit of BI,” emails a retired veteran of multiple technology startups and longtime acquaintance, “is that it has intrinsic value in its own right.”

Data analysts are narrowly focused, and rightly so. They’re looking for the certainty — or at least the illusion of certainty — that numbers provide. Execs — at least the good ones — know they are dealing with a messy and uncertain world. Human beings don’t behave as reliably as numbers. And executive decisions must deal with programs that stretch over many quarters, or even longer. In that time all manner of unplanned events may interfere and require solid executive leadership.

I still think that data analysts will someday find their profession to be a good route to the top. Sales and finance are good paths now, and each tends to breed limited candidates. But until data analysis evolves, it looks like the door to the executive suite may not open as readily as I’d thought it would.

Execs vs. data analysts gives us the Analytics Gap

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One insurance company’s chief data scientist is “terrific at what he does,” says his boss, even “brilliant.” The company is lucky to have him. But he never sees the inside of the executive suite.

“He is a quirky, quirky guy,” says the boss, “and he is a super powerful dude in what he’s doing for us.” But upper executives would judge him harshly. “There would be a terrible outcome.”

Let’s call it the Analytics Gap. At least in this company and probably in many others, some of those who interpret the data don’t stick to rigid, unspoken rules of behavior. And those who with the greatest executive authority can’t quite appreciate what these people can tell them.

“These [analysts], including me,” says the analyst’s boss, “we’re different.” The brilliant data scientist — who as a hobby builds computers from video cards because “‘there’s a lot of power in video cards'” — makes a bad impression in person. He wanders off topic and, for example, has been known to digress into world politics and weird analogies.

“When I hear something come out his mouth that’s a little off color,” the boss says, “I chalk it up to his brilliance.” The company’s top executives, on the other hand, are “a harsh crowd.”

This crowd judges the off-color analyst harshly and fails, for example, to appreciate the real meaning of his risk models and other aspects of advanced analytics. They isolate themselves from the abstract evidence in data analysis that could correct or at least be reminded of their own biases.

The executives tell the analyst’s boss they want that. “I say that if you want that, you have to realize you’re dealing with people whose skill sets are completely different from anything you’ve dealt with. In a perfect world, my chief executive officer would say, ‘I understand how his analysis is applicable.’ No. Nothing.” They just don’t respect what he has to offer.

We’ve seen the work of harsh crowds before. Didn’t we just witness something like this on Wall Street? Those who knew the data saw the mortgage meltdown coming. But those who could have prevented it either dismissed the warnings or misunderstood them. What idiots, we say now. They should be jailed or made to work at Panda Express the rest of their working lives.

Potential consequences of the Analytics Gap are mostly minor by comparison but more widespread and chronic.

“I don’t think it’s a stretch to suggest that micro-versions of [the meltdown] could happen in product development, pricing, marketing, risk, and other domains” — unless data analysts and executive leadership come to understand each other. 

Why not train more quants? It won’t work, he says. Generally, those interested in data analysis tend to be too technically minded for management.